[sacred_numbers]

I can think of a few ways: 1. The ChatGPT web search plugin becomes standard protocol for every prompt. If you ask a factual question ChatGPT will first look up an answer with a search engine, then use the results to craft an answer. This could also be implemented using a knowledge database created by scraping the web that is occasionally updated rather than actually performing a web search every time. This also allows OpenAI to shift the blame to whatever source it found rather than ChatGPT if it does find libelous content. 2. OpenAI will just add a disclaimer to every response (or at least every response that appears to be asking a factual question) saying that asking factual questions is unreliable. 3. If there is a blacklist of topics that require a disclaimer or cause the LLM to refuse to answer, it can generate an answer, check it against the blacklist using embeddings/semantic search and either re-generate an answer or generate a refusal before showing the answer to the user.

My best guess is that it will be a combination of 1 and 2. I have always maintained that LLMs are very unlikely to develop into AGI on their own, but are very likely to be a critical piece of an AGI. The most recent research into scaling laws (Chinchilla, Llama) finds significant improvements from scaling data size much farther than parameter size, so memorizing facts within the parameters will become less and less feasible. This is actually ideal, though, since you want your model parameters to encode language and reasoning patterns, not memorize facts. If it's memorizing facts you either need more data or better (i.e. deduplicated) data. I'm not an expert, though, and I'm too lazy for a research review, so please don't sue me for libel if my facts are out of date.